Journal of applied mathematics & informatics

한국전산응용수학회 (The Korean Society for Computational and Applied Mathematics)
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DISEASE FORECAST USING MACHINE LEARNING ALGORITHMS

  • HUSSAIN, MOHAMMED MUZAFFAR (Department of Computer Science and Engineering, C. Abdul Hakeem College of Engineering and Technology) ;
  • DEVI, S. KALPANA (Department of Computer Science and Engineering, Easwari Engineering College)
  • 투고 : 2022.02.15
  • 심사 : 2022.06.14
  • 발행 : 2022.09.30
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초록

Key drive of information quarrying is to digest liked information starting possible information. With the colossal amount of realities kept in documents, information bases, and stores, in the medical care area, it's inexorably significant, assuming excessive, arising compelling resources aimed at examination besides comprehension like information on behalf of the withdrawal of gen that might assistance in independent direction. Classification is method in information mining; it's characterized as per private, passing on item toward a specific course established happening it is likeness toward past instances of different substances trendy the data collection. In pre-owned recycled four Classification algorithm that incorporate Multi-Layer perception, KSTAR, Bayesian Network and PART to fabricate the grouping replicas arranged the malaria data collection and analyze the replicas, degree their exhibition through Waikato Environment for Knowledge Analysis introduced to Java Development Kit 8, then utilizations outfit's technique trendy promoting presentation of the arrangement methodology. The outcome perceived that Bayesian Network return most elevated exactness of 50.05% when working on followed by Multi-Layer perception, with 49.9% when helping is half, then, at that point, Kstar with precision of 49.44%, 49.5% when supporting individually and PART have lesser precision of 48.1% when helping, The exploration recommended that Bayesian Network is awesome toward remain utilized on Malaria data collection in our sanatoriums.

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